README.md

LayerDiffuse with SDXL for Hugging Face Diffusers

Original paper: Transparent Image Layer Diffusion using Latent Transparency by Lvmin Zhang, Maneesh Agrawala

This is an unofficial port of Layer Diffuse from the SD Forge WebUI extension to Hugging Face Diffusers framework.

This port only focuses on SDXL and transparent PNG image generation.

TL;DR

Install requirements

pip install -r requirements.txt

Run

python demo_sdxl_attn.py \
      --prompt "portrait of woman in suit with messy hair, high resolution, photorealistic, uniform textureless background" \
      --negative_prompt "ugly, bad, shadow, artifact, blurry"

Inference

Snippet

import torch
from diffusers_extension.pipeline_stable_diffusion_xl_layer_diffuse import StableDiffusionXLLayerDiffusePipeline

pipeline = StableDiffusionXLLayerDiffusePipeline.from_pretrained(
    "stabilityai/stable-diffusion-xl-base-1.0",
    torch_dtype=torch.float16,
    variant="fp16",
).to("cuda")

images = pipeline(
    prompt="portrait of woman in suit with messy hair, high resolution, photorealistic, uniform textureless background",
    negative_prompt="ugly, bad, shadow, artifact, blurry",
    num_inference_steps=20,
    width=1024,
    height=1024,
    generator=torch.Generator(device="cuda").manual_seed(42)
).images

images[0].save("sdxl_layerdiffuse_result.png")

Demo

Check out demo_sdxl_attn.py for the complete demo.

Full arguments list:

python demo_sdxl_attn.py \
      --seed SEED \
      --batch_size BATCH_SIZE \
      --guidance_scale GUIDANCE_SCALE \
      --num_inference_steps NUM_INFERENCE_STEPS \
      --width WIDTH \
      --height HEIGHT \
      --prompt PROMPT \
      --negative_prompt NEGATIVE_PROMPT \
      --output_path OUTPUT_PATH \
      --disable_memory_optim

Implementation details

I created a class StableDiffusionXLLayerDiffusePipeline (code here) deriving from diffusers.StableDiffusionXLPipeline.

StableDiffusionXLLayerDiffusePipeline can still be initialized with .from_pretrained(), as one would do for diffusers.StableDiffusionXLPipeline.

Additionally, this new class takes care of:

• Loading the rank-256 LoRA layer_xl_transparent_attn.safetensors to turn SDXL into a transparent image generator
  • It will change the latent distribution of the model to a "transparent latent space" that can be decoded by the special VAE pipeline
• Loading vae_transparent_decoder.safetensors
  • This is an image decoder that takes SDXL VAE outputs and latent image as inputs, and outputs a real PNG image
• Overload the pipeline's __call__() method to automatically forward the output of SDXL (latent + image w/ uniform background) to the VAE Transparent Decoder